Spin glasses -- theoretical models used to capture several physical properties of real glasses -- are mostly studied by Monte Carlo simulations. The associated algorithms have a very large and easily identifiable degree of available parallelism, that can also be easily cast in SIMD form. State-of-the-art multi-and many-core processors and accelerators are therefore a promising computational platform to support these Grand Challenge applications. In this paper we port and optimize for many-core processors a Monte Carlo code for the simulation of the 3D Edwards Anderson spin glass, focusing on a dual eight-core Sandy Bridge processor, and on a Xeon-Phi co-processor based on the new {\em Many Integrated Core} architecture. We present performance results, discuss bottlenecks preventing further performance gains and compare with the corresponding figures for GPU-based implementations and for application-specific dedicated machines.

### Benchmarking MIC architectures with Monte Carlo simulations of spin glass systems

#### Abstract

Spin glasses -- theoretical models used to capture several physical properties of real glasses -- are mostly studied by Monte Carlo simulations. The associated algorithms have a very large and easily identifiable degree of available parallelism, that can also be easily cast in SIMD form. State-of-the-art multi-and many-core processors and accelerators are therefore a promising computational platform to support these Grand Challenge applications. In this paper we port and optimize for many-core processors a Monte Carlo code for the simulation of the 3D Edwards Anderson spin glass, focusing on a dual eight-core Sandy Bridge processor, and on a Xeon-Phi co-processor based on the new {\em Many Integrated Core} architecture. We present performance results, discuss bottlenecks preventing further performance gains and compare with the corresponding figures for GPU-based implementations and for application-specific dedicated machines.
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Spin Glass; Many core architectures; Monte Carlo Simulations
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1875516
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